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- W3017147957 abstract "We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimise the hedging error instead of the pricing error. Applied to end-of-day and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the Black-Scholes benchmark significantly. We illustrate, however, that a similar benefit arises by simple linear regressions that incorporate the leverage effect. Finally, we show how a faulty training/test data split, possibly along with an additional 'tagging' of data, leads to a significant overestimation of the outperformance of neural networks." @default.
- W3017147957 created "2020-04-24" @default.
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- W3017147957 date "2020-04-19" @default.
- W3017147957 modified "2023-09-27" @default.
- W3017147957 title "Hedging with Neural Networks." @default.
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